Paper: Edit Detection And Parsing For Transcribed Speech

ACL ID N01-1016
Title Edit Detection And Parsing For Transcribed Speech
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

We present a simple architecture for parsing transcribed speech in which an edited-word de- tector rst removes such words from the sen- tence string, and then a standard statistical parser trained on transcribed speech parses the remaining words. The edit detector achieves a misclassi cation rate on edited words of 2.2%. (The NULL-model, which marks everything as not edited, has an error rate of 5.9%). To evalu- ate our parsing results we introduce a new eval- uation metric, the purpose of which is to make evaluation of a parse tree relatively indi erent to the exact tree position of EDITED nodes. By this metric the parser achieves 85.3% precision and 86.5% recall.